Necessary in the art of image modeling is an accurate, physically-based, computationally feasible probabilistic model for ultrasonic images. Model-based image analysis has become increasingly popular and successful to the analysis of object shape in images. Traditional approaches, usually termed pattern or object recognition, typically involve building a pattern or object from features extracted by “processing” the image. In the model-based approach, shape is modeled rigorously then inferred using an image model. Shape can be inferred in a Bayesian setting by combining a probabilistic prior model describing variation of the shape with a data likelihood (i.e., a probabilistic model describing observations of the shape). The physics of the imaging system are incorporated using the data likelihood. Model-based approaches can provide insight and understanding regarding the process of image formation and its connection to underlying structure, thereby forming a solid foundation for inference of structural patterns in image data.
Shape is represented in ultrasound as the result of complex interactions at the microstructural level, producing a mix of speckle texture and coherent echoes. Application of model-based techniques, therefore, require a probabilistic model unique to ultrasound that accurately represents shape in terms of these interactions. Previous ultrasound applications of model-based approaches to shape analysis have been minimal and have used simplistic data models (e.g., Rayleigh with a constant parameter over a given contour). Previous applications have shown some success with simple shape models, although only in high contrast images. Because previous applications are not physically-based, the results are not extendable to other image analysis problems and little intuition or insight is developed for representing and understanding the relation between the data and the underlying shape.
For the foregoing reasons, there is a need for an accurate, physically-based, computationally-feasible probabilistic model for ultrasonic images incorporating shape, microstructure and system characteristics.